Fig 3 - by Maryam Shadi (14237349)

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Fig 8 - by Maryam Shadi (14237349)

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Fig 6 - by Maryam Shadi (14237349)

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Fig 11 - by Maryam Shadi (14237349)

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Schematic of the suggested ROSA network for 16-element linear array consisting of power division, phase control, amplitude control of primary and secondary array. by Maryam Shadi (14237349)

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Normalized radiation patterns with GAPSO amplitude and phase tapering for ± 25° scan for aperiodic array in 28 GHz. by Maryam Shadi (14237349)

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Guideline of the proposed methodology. by Maryam Shadi (14237349)

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Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

Datasets for "Continental fragments in the South China Block – constraints from crustal radial anisotropy" by Huaiyu Yuan (15355378)

“…</p> <p><br></p> <p>With these cross-functions, following a standard ambient noise inversion procedure, i.e., measuring surface wave dispersion for both Rayleigh and Love waves, developing dispersion models for both waves, and appying a joint inverting, the radial anisotropic crustal shear wave model presented in our study can be reproduced. …”

Parameter definitions and estimation for functions <i>c</i>_<i>i</i>(<i>t</i>) and <i>q</i>_<i>i</i>(<i>t</i>) (i = 1,2,3). by Mengqi He (5753525)

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Systematically Improvable Tensor Hypercontraction: Interpolative Separable Density-Fitting for Molecules Applied to Exact Exchange, Second- and Third-Order Møller–Plesset Perturbat... by Joonho Lee (1351974)

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Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …”

RMSE results. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Results of the Kherson Area Visual Assessment. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Work flow chart. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Experimental data. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Results of the PY area visual assessment. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Main parameters of braking system. by Honglei Pang (22693724)

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Simulation Results Plot of the SBW System Response: (a) Step response curve of the SBW system; (b) Sine response curve of the SBW system; (c) Steady state error curve of the SBW sy... by Honglei Pang (22693724)

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EMB and SBW system structure. by Honglei Pang (22693724)

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